OLED Compensation Data Compression Using Trend and Noise Separation
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Solution Overview
Problem
Conventional compression methods for compensation data in organic light emitting diode display devices require large memory capacity, increasing manufacturing costs due to data loss and reduced image quality when compressing high-frequency components.
Innovation Solution
The method involves compressing the noise component with a larger quantization step value and maintaining the trend component without quantization, reducing losses and improving compression ratios, thereby reducing memory capacity and manufacturing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional compression methods are used for compensation data, then compression ratio is improved, but image quality deteriorates due to data loss in high-frequency components
Solution Approach 1:
The patent segments compensation data into two distinct components: trend component (low-frequency) and noise component (high-frequency). This segmentation allows different compression strategies to be applied to each component, preserving the important trend information while compressing the noise component more aggressively, thus resolving the contradiction between maintaining image quality and achieving high compression ratio.
Solution Approach 2:
The patent applies different quality standards to different parts of the compensation data. The trend component is preserved with high fidelity (minimal quantization) to maintain image quality, while the noise component is compressed with lower fidelity (larger quantization steps) to achieve higher compression ratio. This local differentiation of quality requirements resolves the contradiction between preserving information and improving compression.
2Reliability
If large memory capacity is used to store compensation data, then image quality is maintained, but manufacturing cost increases
Solution Approach 1:
By segmenting compensation data into trend and noise components and applying differential compression, the patent significantly reduces the memory capacity required to store compensation data. This reduction in storage requirements directly lowers manufacturing costs while the preserved trend component maintains sufficient image quality, thus resolving the contradiction between reliability and ease of manufacture.
Solution Approach 2:
The patent changes the parameter of data representation by separating compensation data into different frequency components and applying different compression levels. This parameter transformation allows the system to use smaller memory capacity while maintaining acceptable image quality through intelligent data management, thereby reducing manufacturing costs without sacrificing reliability.
3Productivity
If quantization is applied to all compensation data, then compression ratio is improved, but image quality deteriorates due to loss in trend component
Solution Approach 1:
The patent segments compensation data into trend component and noise component, allowing selective application of quantization. The trend component undergoes minimal or no quantization to preserve image quality, while the noise component undergoes aggressive quantization to achieve high compression ratio. This selective segmentation approach resolves the contradiction between compression ratio and manufacturing precision.
Solution Approach 2:
The patent applies different quantization quality levels to different parts of the compensation data locally. The trend component receives high-quality treatment (minimal quantization) to maintain manufacturing precision, while the noise component receives low-quality treatment (strong quantization) to improve compression ratio. This local quality differentiation resolves the contradiction between productivity and manufacturing precision.
Data Source
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AI summary
A method of compressing data comprising separating a bit stream of compensation data into a sub-bit stream including an impulse component and a sub-bit stream not including the impulse component, predicting and calculating a trend component of compensation data to be compressed using compressed compensation data, generating a noise component by eliminating the trend component from the compensation data, and separating and compressing an impulse component and a noise component of the compensation data from each other, wherein the impulse component is compressed without quantization and the noise component is quantized and compressed.